An important aspect of medical care is disease management. For effective disease management, it is not only important to understand the cause of any medical episodes related to the underlying medical conditions and taking suitable actions, but it is equally important to be able to predict any future episodes requiring medical intervention beforehand.
With the advent of sensors and health monitoring technologies that are user friendly, the collection of health and behavior data has increased significantly. However, prevailing methods fail to accurately predict future episodes requiring medical intervention.
For example, diabetes is a result of poor control over one's blood glucose (BG) levels. Two major forms of diabetes are type 1 and type 2. Type 1 occurs from the body's failure to produce insulin, and requires the person to inject insulin or wear an insulin pump. Type 2 is a result of body's resistance to insulin, a condition in which cells do not properly use insulin, sometimes combined with an absolute insulin deficiency.
One of the major complications of diabetes resulting in acute episodes is called hypoglycemia. An episode refers to when a person's BG level drops below approximately 70 mg/dL. Such an episode results in inadequate supply for glucose to the brain which may cause seizures, unconsciousness, mild dysphoria, and may even result in death if not treated immediately. Hypoglycemia may be caused by numerous factors including an insulin overdose, inadequate nutrition, lack of nutrition before exercise, natural metabolism, abnormal blood glucose values, amount of nutrition, and medication.
Hypoglycemia may be treated by restoring BG levels to normal by medication, nutrition or glucose tablets. Major factors causing hypoglycemia are overdosing of medications, inadequate nutrition, and lack of nutrition before exercise and due to one's metabolism. However, data related to such factors is often not available from patients. While prevailing techniques use continuous glucose monitoring devices (CGM) to immediately respond to such episodes. These techniques do not allow for accurate predictions of future hypoglycemic episodes based on patient-reported self-monitored blood glucose (SMBG) data.